Using advanced shipping notification information for supply chain process analysis

ABSTRACT

A product supply chain may be viewed not just as a series of discrete, unrelated shipment transactions, but as a “process” (or pipeline) that can be subject to statistical process control. The present invention is directed to novel systems and methods for collecting data concerning one or more aspects of a supply chain, for performing statistical analysis on the collected data to facilitate the identification of anomalies or inefficiencies in the process, and for communicating the results of such statistical analysis to those responsible for the supply chain so that remedial measures may be taken, if appropriate. Among other things, a method is disclosed that involves storing, in memory accessible to at least one processor, an electronic copy of an advanced shipping notification (ASN) that was transmitted from a shipping location to a receiving location in advance of shipping at least one item therebetween, and also storing, in memory accessible to the at least one processor, data reflecting at least one determined value of at least one monitored aspect of the shipment between the shipping location and the receiving location. The at least one processor uses at least some information from the ASN stored in memory, together with at least a portion of the data stored in memory, to generate at least one report involving the at least one determined value.

This application claims the benefit of each of: (1) U.S. ProvisionalApplication Ser. No. 60/500,565, filed Sep. 5, 2003, (2) U.S.Provisional Application Ser. No. 60/519,458, filed Nov. 12, 2003, (3)U.S. Provisional Application Ser. No. 60/526,878, filed Dec. 4, 2003,(4) U.S. Provisional Application Ser. No. 60/564,402, filed Apr. 22,2004, and (5) U.S. Provisional Application Ser. No. 60/564,447, filedApr. 22, 2004. The entire disclosure of each of the foregoingapplications is hereby incorporated herein by reference.

The present invention is directed generally to novel systems and methodsfor collecting data concerning one or more aspects of a supply chain andperforming a statistical analysis of the supply chain process based onthat data.

BACKGROUND

For many companies, one of the most important business processes is themovement of perishable goods through the temperature-controlled portionsof a supply chain. In many instances, proper operation of the so-called“cold chain” is critical to maintaining the quality of such goods.

Each year, companies spend millions of dollars on refrigerated stagingand processing areas, storage, transport, display and/or specializedpackaging. Many of them, however, do not know how well the entire, andvery expensive, process works.

Traditional temperature monitoring programs are generally used to makeaccept/reject decisions or for dispute resolution. They are typicallynot designed to help a company identify and address problems orinefficiencies in its cold chain. Measuring the effectiveness of thecold chain can enable a company to identify and address such problems orinefficiencies, which might otherwise go undetected.

FIG. 1 is a block diagram of a prior art system 100 for cold chainanalysis that has been employed in the past by the assignee of thepresent invention. As shown, the system 100 included a temperaturesensor 102 which was associated with a quantity of product 104 as theproduct 104 was transported from a shipping location 106 to a receivinglocation 108. At the shipping location 106, a hand-held data entrydevice 110 would be used to manually enter data concerning the product104 and the temperature sensor 102 associated with it. The data enteredin the device 110 was then transmitted to a database 112 at a remotelocation 114. In some circumstances, the shipper would insteadcommunicate information associating the sensor 102 with the product 104to the remote location 114 in another manner, such as (1) by sending apacking list for the product, including the sensor ID, by facsimile tothe remote location 114, (2) by sending a database containing suchinformation to the remote location 114 on a regular basis, e.g.,monthly, or (3) by simply writing product identification information onthe sensor 102.

Before the product 104 left the shipping location 106, a person manuallydepressed a “start” button on the sensor 102 so as to cause the sensor102 to begin logging temperature information at regular intervals, e.g.,every five minutes.

After the sensor 102 reached the receiving location 108, someone wouldeither download the information from the sensor 102 via a downloadingdevice 116 and transmit the downloaded data to the database 112 at theremote location 1 14, or would ship the sensor 102 to the remotelocation 114, where data would then be downloaded from it and uploadedto the database 112. At this time, the data collected by the sensor 102would be associated with the shipment information gathered by thehandheld device 110 so as to create a complete record containinginformation about the shipment, its contents, and the data recorded byall associated sensors.

Some sensors 102 were equipped with “stop” buttons, which may or may nothave been activated when the sensors 102 and associated products 104reached the receiving location 108.

FIG. 2 shows an example of how the data would typically appear when itwas first uploaded to the database 112. Since all of the uploaded datawas not necessarily accumulated during a period of interest, e.g., thesensor 102 may have been started before it was associated with a product104 or before the product 104 had entered the supply chain beingmonitored, or may have been stopped after it was separated from theproduct 104 or after the product 104 had exited the supply chain beingmonitored, the data had to be “conditioned” so as to reflect only theperiod of interest. This conditioning was done manually based on theinformation that was made available to, and the past experience of, theperson performing the conditioning, and therefore required that personto make an educated guess concerning the portion of the data that shouldproperly be used for further analysis.

In the example shown in FIG. 2, it appears likely that the sensor 102was started several hours before it was associated with a product 104 inthe cold chain, and was stopped or had data extracted from itapproximately one half hour after it was separated from the product 104.Thus, if this raw (unconditioned) data had been used to perform any typeof analysis, the results of that analysis would have been inaccurate andunreliable.

FIG. 3 illustrates an example of how beginning and ending locations forthe raw data from a sensor 102 were marked during the conditioning step,with the line 302 identifying the beginning point and the line 304identifying the ending point 304. To determine how the data should beconditioned, a person had to examine the raw data to identify telltalecharacteristics indicating the likely points where the sensor 102entered and left the cold chain, and correlate those points with anyother information known about the shipment, such as how long suchshipments normally took, the approximate times that the shipments beganand ended, etc., so as to permit the person to make an educated guess asto where to place the lines 302, 304.

After the data from a sensor 102 had been conditioned, summary data forthe entire shipment could be extracted from it into a spreadsheetformat. Such summary data generally included parameters such as shipmentID, origin, destination, carrier, product type, average temperature,minimum temperature, maximum temperature, time above the idealtemperature, time below the ideal temperature, trip time, and degreeminutes (i.e., the total number of minutes at each degree). Afterextraction, the summary data could be accessed and processed by a humanbeing using special software programs to manually generate informativegraphs and charts.

After these graphs and charts were generated, the human being had tomanually transfer them to a server 126 and thereby make them accessibleto a customer's computer 128 (FIG. 1) via the Internet 122 for reviewand analysis. That is, after logging onto a website maintained on theserver 126, a customer at the computer 128 could click on one of apredefined set of hyperlinks to access a corresponding, pre-generatedchart or graph stored on the server 126. In some instances, afteraccessing such an initial chart or graph, the customer could “drilldown” to additional pre-defined charts or graphs stored on the server126 by clicking on a particular part of the initial chart or graph.

Descriptions of three types of charts and graphs that were generated inthis prior art system (a box plot, a control chart, and a histogram) areprovided below in connection with FIGS. 4-6. Turning first to FIG. 4, anexample is shown of how a box plot 400 for accumulated data wasgenerated. Such a box plot provided a summary of the location, spread,and skewness of a distribution. In the box plot 400, the upper quartile(Q3 or 75th percentile) and the lower quartile (Q1 or 25th percentile)of the data are portrayed by the top 410 and bottom 412, respectively,of a rectangular box 408. Roughly 50% of the data is contained withinthe box 408. The median (50th percentile) is portrayed by a horizontalline segment 402 within the box 408.

The lines 404 that extend from the ends 410, 412 of the box 408, calledthe “whiskers,” are based on the data in the tails of the data and helpshow the spread of the distribution. The whiskers 404 extend to the mostextreme point within a calculated range (e.g., one and one-half timesthe distance between the ends 410, 412) beyond each end 410, 412 of thebox 408. Points falling beyond the whiskers are indicated by individualstars 406. These points are potential outliers, since they aresignificantly different from the rest of the observations.

If the median line 402 cut the box 408 in half and the whiskers 404 oneither end 410, 412 extended about the same distance from the box 408,then the distribution was symmetrical. Lack of symmetry would indicatethat the data may not have come from a normal distribution. If the datawas normally distributed, then roughly 99% of the data would have beencontained between the whiskers 404 of the box plot 400.

FIG. 5 shows an example of a control chart 500 generated using the priorart system discussed above. Such control charts helped the userunderstand what their current supply chain processes were capable of andhow much variation (about the mean) to expect from the current process.Such charts also allowed the user to determine whether variations frompoint to point were due to expected random variation or due to anassignable cause. Basically, a control chart is a run chart thatincludes statistically generated upper and lower control limits. Thepurpose of a control chart was to detect any unwanted changes in theprocess being analyzed. Abnormal points or certain patterns in the graphsignal would reveal these changes.

Extensive research by statisticians has shown that by establishing upperand lower limits at three times the standard deviation of the process(plus and minus, respectively), 99.73% of the random variation wouldfall within these limits. When a point falls outside the control limitsor when certain patterns occur in the data, it is usually due to anassignable cause. A process is therefore said to be in “statisticalcontrol” when the process measurements vary randomly within the controllimits; that is, the variation present in the process is consistent andpredictable over time. The upper and lower control limits are not thesame as tolerance or specification limits. Control limits are a functionof the way a process actually performs over time. Specification, ortolerance, limits, on the other hand, are a function of what people wantthe process to do, and may not have any direct relationship to theactual capabilities of the process.

Control charts such as that shown in FIG. 5 have three basic components:(1) performance data 502 plotted over time, (2) a centerline (CL) 504,which is the mathematical average of all the samples plotted, and (3)upper 506 and lower 508 statistical control limits (UCL & LCL) thatdefine the constraints of common cause variations.

In the prior art system 100 discussed above, the control limits 506, 508were used in conjunction with control charts 500 to help interprettemperature-related data accumulated by sensors 102. Control limits 506,508 reflected the expected variation in the temperature of the coldchain being monitored. Results that fell outside of these limits wereconsidered to be “out of control” points and would have suggested thatsome abnormal cause had acted on the cold chain. If the temperature datafluctuated within the limits 506, 508, it was assumed to be the resultof common causes within the process (flaws inherent in the process), andcould only have been affected if the system was improved or changed. Ifthe temperature data fell outside of the limits 506, 508, it was assumedto be the result of special causes.

In the prior art system 100, several tests were used to spot variationsdue to assignable causes on a control chart 500: (1) one data pointfalling outside the control limits, (2) six or more points in a rowsteadily increasing or decreasing, (3) eight or more points in a row onone side of the centerline, and (4) fourteen or more points alternatingup and down. These tests were conducted manually by a human being. Thatis, a human being had to visually inspect the control charts for thepresence of one or more of the above patterns.

FIG. 6 shows an example of a histogram 600 generated using the prior artsystem discussed above. A histogram, such as the histogram 600, is agraphical representation of a set of measurements. It consists ofcolumns drawn over class intervals, the heights of which areproportional to the number of measurements falling within a giveninterval.

A histogram is constructed from a frequency table. A frequency table isconstructed by dividing the data collected into intervals or “bins,” andthen counting the number of data points falling within each interval. Agraph consisting of several columns is then constructed, with the heightof each column being determined by the number of data points collectedin a corresponding interval. In the histogram 600, the height of eachcolumn indicates the number of shipments during which the measuredminimum temperature fell within a respective five degree range oftemperatures.

The following example is illustrative of how the above-described chartsand graphs stored on the server 126 were accessed by a customer usingthe prior art system 100. First, after logging onto a website maintainedby the server 126, the customer could select a hyperlink correspondingto a particular characteristic (e.g., mean temperature) from among agroup of such hyperlinks (e.g., mean temperature, minimum temperature,maximum temperature, degree-minutes >0° F., time >0° F., and trip time).In response to that selection, a pre-selected and pre-generated group ofbox plots 400 stored on the server 126 could be displayed that weresegregated according to some pre-defined criterion (e.g., by thedistribution centers through which monitored product flowed). The usercould then click on one of the box plots 400 to access a pre-generatedcontrol chart 500 stored on the server 126 that included a set of datapoints upon which the selected box plot was based. The user could thenclick on one of the data points 502 in the control chart 500 to retrievefrom the server 126 either a graph of data from, or a summary chart ofinformation relating to, the trip with which the selected data point 502corresponded. In some prior art systems the customer could also retrievefrom the server 126 a pre-generated histogram 600 for the originallyselected criterion, e.g., mean temperature.

In such systems, all of the charts and graphs available for display tothe customer were generated and stored on the server 126 sometime beforethe customer logged onto the website. Once uploaded to the server 126,the content of, and available formats for, those charts and graphs wasessentially fixed unless and until new or different charts and graphswere separately generated and uploaded to the server 126. Thus, whilethe customer was able to select from among a number of pre-selected andpre-generated charts and graphs that had been uploaded to the server126, the customer's control of (1) which charts and graphs weregenerated and therefore available for review, (2) the content of theavailable charts and graphs, and (3) the format in which the availablecharts or graphs were presented, was limited to requesting thatadditional or different charts or graphs be manually created and madeavailable via additional hyperlinks on the website.

Returning again to FIG. 1, for purposes entirely unrelated to themonitoring of temperature, in advance of sending a shipment of product104, a computer 118 at the shipping location 106 typically sent anadvanced shipping notification (ASN), sometimes alternatively referredto as “dispatch advice,” an “advance ship notice,” a “bill of lading,” a“ship notice/manifest,” etc., over the Internet 122 to a computer 120 atthe receiving location 108. As used herein, “advanced shippingnotification” is intended to broadly encompass all such notice types. Anexample of an extensible markup language (XML) file structure for an ASNis attached as Appendix A, to provisional application Ser. No.60/500,565, incorporated by reference above. Another example of an ASNfile structure is set forth in electronic data interchange (EDI)transaction set 856. In addition to other pieces of information, the ASNgenerally included a tracking number for the shipment. With the trackingnumber, either of the computers 118 or 120 could access a server 124maintained by the carrier to track the product 104 through the coldchain. Operators of the computers 118 and 120 could therefore retrievefrom the server 124 information such as when the product 104 left theshipping location 106 and arrived at the receiving location 108, as wellas occasions on which the product 104 reached certain transition pointsalong the way that were recorded by the carrier.

SUMMARY

A product supply chain may be viewed not just as a series of discrete,unrelated shipment transactions, but as a “process” (or pipeline) thatcan be subject to statistical process control. The present invention isdirected to novel systems and methods for collecting data concerning oneor more aspects of a supply chain, for performing statistical analysison the collected data to facilitate the identification of anomalies orinefficiencies in the process, and for communicating the results of suchstatistical analysis to those responsible for the supply chain so thatremedial measures may be taken, if appropriate.

According to one aspect of the present invention, a method involvesstoring, in memory accessible to at least one processor, an electroniccopy of an advanced shipping notification (ASN) that was transmittedfrom a shipping location to a receiving location in advance of shippingat least one item therebetween, and also storing, in memory accessibleto the at least one processor, data reflecting at least one determinedvalue of at least one monitored aspect of the shipment between theshipping location and the receiving location. The at least one processoruses at least some information from the ASN stored in memory, togetherwith at least a portion of the data stored in memory, to generate atleast one report involving the at least one determined value.

According to another aspect, a database has stored therein an electroniccopy of an advanced shipping notification (ASN) that was transmittedfrom a shipping location to a receiving location in advance of shippingat least one item therebetween, and further has stored therein datareflecting at least one determined value of at least one monitoredaspect of the shipment of the at least one item.

According to another aspect, a computer-readable medium is disclosed foruse with at least one processor included in a system including at leastone memory accessible to the at least one processor that has storedtherein an electronic copy of an advanced shipping notification (ASN)that was transmitted from a shipping location to a receiving location inadvance of shipping at least one item therebetween, and having furtherstored therein data reflecting at least one determined value of at leastone monitored aspect of the shipment of the at least one item. Thecomputer-readable medium has a plurality of instructions stored thereonwhich, when executed by the at least one processor, cause the at leastone processor to use at least some information from the ASN stored inmemory, together with at least a portion of the data stored in memory,to generate at least one report involving the at least one determinedvalue.

According to another aspect, a method involves transmitting an advancedshipping notification (ASN) from a shipping location to a receivinglocation in advance of shipping at least one item therebetween. The ASNcomprises an identifier that identifies a sensor that will be used tomonitor at least one physical or environmental condition of the at leastone item as the item is transported between the shipping location andthe receiving location.

According to another aspect, an advanced shipping notification (ASN),which is transmitted from a shipping location to a receiving location inadvance of shipping at least one item therebetween, comprises anidentifier that identifies a sensor that will be used to monitor atleast one physical or environmental condition of the at least one itemas the item is transported between the shipping location and thereceiving location.

According to another aspect of the present invention, a method forperforming statistical analysis on at least one monitored aspect of aproduct supply chain involves storing, in memory accessible to at leastone processor, first data reflecting at least one first determined valueof at least one monitored aspect of a first shipment of at least onefirst item occurring in the supply chain, and storing, in memoryaccessible to the at least one processor, second data reflecting atleast one second determined value of at least one monitored aspect of asecond shipment of at least one second item occurring in the supplychain. The at least one processor is used to automatically generate atleast one report reflecting a statistical analysis of the first data andthe second data.

According to another aspect, a system for performing statisticalanalysis on at least one monitored aspect of a product supply chaincomprises at least one memory and at least one processor. The at leastone memory has stored therein first data reflecting at least one firstdetermined value of at least one monitored aspect of a first shipment ofat least one first item occurring in the supply chain, and havingfurther stored therein second data reflecting at least one seconddetermined value of at least one monitored aspect of a second shipmentof at least one second item occurring in the supply chain. The at leastone processor is coupled to the at least one memory and is configured toautomatically generate at least one report reflecting a statisticalanalysis of the first data and the second data.

According to another aspect, a system for performing statisticalanalysis on at least one monitored aspect of a product supply chaincomprises means for storing first data reflecting at least one firstdetermined value of at least one monitored aspect of a first shipment ofat least one first item occurring in the supply chain, and means forstoring second data reflecting at least one second determined value ofat least one monitored aspect of a second shipment of at least onesecond item occurring in the supply chain. The system further comprisesmeans, coupled to the means for storing first data and the means forstoring second data, for automatically generating at least one reportreflecting a statistical analysis of the first data and the second data.

According to another aspect, a computer-readable medium is disclosed foruse with at least one processor included in a system including at leastone memory having stored therein first data reflecting at least onefirst determined value of at least one monitored aspect of a firstshipment of at least one first item occurring in a supply chain, andhaving further stored therein second data reflecting at least one seconddetermined value of at least one monitored aspect of a second shipmentof at least one second item occurring in the supply chain. Thecomputer-readable medium has a plurality of instructions stored thereonwhich, when executed by the at least one processor, cause the at leastone processor to automatically generate at least one report reflecting astatistical analysis of the first data and the second data.

According to another aspect of the present invention, a method foranalyzing a supply chain process involves automatically determiningwhether a plurality of data points, each representing at least onedetermined value of at least one monitored aspect of a supply chain,match a pattern indicative of an anomalous condition in the supply chainprocess.

According to another aspect, a system for analyzing a supply chainprocess comprises at least one processor configured to automaticallydetermine whether a plurality of data points, each representing at leastone determined value of at least one monitored aspect of a supply chain,match a pattern indicative of an anomalous condition in the supply chainprocess.

According to another aspect, a computer-readable medium is disclosed foruse with at least one processor included in a system including at leastone memory having stored therein data representing a plurality of datapoints, each data point representing at least one determined value of atleast one monitored aspect of a supply chain. The computer-readablemedium has a plurality of instructions stored thereon which, whenexecuted by the at least one processor, cause the at least one processorto automatically determine whether the plurality of data points match apattern indicative of an anomalous condition in the supply chainprocess.

According to another aspect, a method for analyzing a supply chainprocess involves automatically determining whether any of a plurality ofdata points representing determined values of at least one monitoredaspect of a supply chain falls without statistical process controllimits for the supply chain process.

According to another aspect, a system for analyzing a supply chainprocess comprises at least one processor configured to automaticallydetermine whether any of a plurality of data points representingdetermined values of at least one monitored aspect of a supply chainfalls without statistical process control limits for the supply chainprocess.

According to another aspect, a computer-readable medium is disclosed foruse with at least one processor included in a system including at leastone memory having stored therein data representing a plurality of datapoints, with each data point representing at least one determined valueof at least one monitored aspect of a supply chain. Thecomputer-readable medium has a plurality of instructions stored thereonwhich, when executed by the at least one processor, cause the at leastone processor to automatically determine whether any of the plurality ofdata points falls without statistical process control limits for thesupply chain process.

According to another aspect of the present invention, a method isdisclosed for use in a system in which at least one first sensor isassociated with at least one first item that is transported from a firstshipping location to a first receiving location so that the at least onefirst sensor can monitor at least one physical or environmentalcondition of the at least one first item as the at least one first itemis so transported. The method comprises storing first data accumulatedby the at least one first sensor in memory, with at least some of thefirst data reflecting changes in the at least one physical orenvironmental condition of the at least one first item that occurredwhen the at least one first sensor was associated with the at least onefirst item, and automatically identifying at least a portion of thefirst data as having been accumulated when the at least one first sensorwas associated with the at least one first item.

According to another aspect, a computer-readable medium is disclosed foruse with at least one processor included in a system in which at leastone first sensor is associated with at least one first item that istransported from a first shipping location to a first receiving locationso that the at least one first sensor can monitor at least one physicalor environmental condition of the at least one first item as the atleast one first item is so transported, and in which first dataaccumulated by the at least one first sensor is stored in memory, withat least some of the first data reflecting changes in the at least onephysical or environmental condition of the at least one first item thatoccurred when the at least one first sensor was associated with the atleast one first item. The computer-readable medium has a plurality ofinstructions stored thereon which, when executed by the at least oneprocessor, cause the at least one processor to automatically identify atleast a portion of the first data as having been accumulated when the atleast one first sensor was associated with the at least one first item.

According to another aspect of the present invention, a method involvesstoring, in memory accessible to at least one processor, data reflectingat least one determined value of at least one monitored aspect of ashipment of at least one item from a shipping location to a receivinglocation, and also storing, in memory accessible to the at least oneprocessor, location event information corresponding to the shipment ofthe at least one item from the shipping location to the receivinglocation. The at least one processor uses the location event informationstored in memory, together with the data stored in memory, to generateat least one report involving the at least one determined value.

According to another aspect, a database has stored therein datareflecting at least one determined value of at least one monitoredaspect of a shipment of at least one item from a shipping location to areceiving location, and has further stored therein location eventinformation corresponding to the shipment of the at least one item fromthe shipping location to the receiving location.

According to another aspect, a computer-readable medium is disclosed foruse with at least one processor included in a system having at least onememory accessible to the at least one processor having stored thereindata reflecting at least one determined value of at least one monitoredaspect of a shipment of at least one item from a shipping location to areceiving location, and having further stored therein location eventinformation corresponding to the shipment of the at least one item fromthe shipping location to the receiving location. The computer-readablemedium has a plurality of instructions stored thereon which, whenexecuted by the at least one processor, cause the at least one processorto use the location event information stored in memory, together withthe data stored in memory, to generate at least one report involving theat least one determined value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a prior art system for monitoringtemperature conditions during one or more shipments of product;

FIG. 2 is a graph showing a typical waveform that was output by one ofthe sensors shown in the system of FIG. 1;

FIG. 3 is a graph showing how conditioning lines were manually added tographs like that shown in FIG. 2;

FIG. 4 shows an example of a box plot generated based upon a statisticalanalysis of accumulated data;

FIG. 5 shows an example of a control chart generated based upon astatistical analysis of accumulated temperature data;

FIG. 6 shows an example of a histogram generated based upon astatistical analysis of accumulated temperature data;

FIG. 7 shows an example embodiment of a system embodying several aspectsof the present invention;

FIG. 8 shows an example user interface screen that may be presented to auser to permit the user to select various parameters upon which one ormore reports reflecting a statistical analysis of a supply chain will bebased;

FIG. 9 shows an example of a set of box plots that may be automaticallygenerated based upon a statistical analysis of accumulated temperaturedata in accordance with one aspect of the invention;

FIG. 10 shows an example of a control chart that may be automaticallygenerated based upon a statistical analysis of accumulated temperaturedata in accordance with one aspect of the invention;

FIG. 11 shows an example of a temperature graph for a single shipmentthat may, for example, be accessed by clicking on one of the points inthe control chart of FIG. 10;

FIG. 12 shows an example of a histogram that may be automaticallygenerated based upon a statistical analysis of accumulated temperaturedata in accordance with one aspect of the invention; and

FIG. 13 shows an example of temperature graph that may presented to theuser which reflects location event information in accordance with oneaspect of the present invention.

DETAILED DESCRIPTION

As noted above, a product supply chain may be viewed not just as aseries of discrete, unrelated shipment transactions, but as a “process”(or pipeline) that can be subject to statistical process control. Asused herein, “supply chain” refers to any mechanism by which product istransported between at least two points, and may encompass any situationin which one or more product types are transported by one or morecarriers from one or more origins, either directly or through one ormore distribution centers or intermediate holding locations, to one ormore destinations, or even just some portion or leg of the foregoing,e.g., from a particular distribution center to a particular destination.It is not uncommon in a supply chain for a quantity of product to betransferred in bulk during one or more initial legs, to be broken downinto pallets for transfer during one or more additional legs, and thenfurther broken down into cartons or mixed with other products fortransfer during one or more final legs. This entire series of transferswould be considered to be a “supply chain” as that term is used herein,as would any single portion or leg, or combination of portions or legs,of such a series of transfers.

By regularly accumulating data concerning one or more aspects of variousshipments in a supply chain, and making reports based on a statisticalanalysis of that data readily accessible to a user, the supply chain“process” can be efficiently and effectively analyzed by the user. Byusing proper analytical techniques, various inefficiencies and/oranomalies in the process can be brought to light in such reports, sothat measures can be taken promptly and efficiently to ameliorate thesame.

In some embodiments, the data accumulation and/or report generationprocesses may be fully automated so that reports can be generatedautomatically (by one or more computers) based on accumulated data anytime a user electronically submits a request for such a report. As usedherein, “automated” and “automatically” are used to refer to any actionor set of actions that are taken without human intervention, e.g., acomputer-implemented process. That a human being requests a computer toperform a process does not mean that the process performed by thecomputer in response to that request is not automated.

Reports may additionally or alternatively be generated automatically ona regular basis (e.g., once a day, once a week, once a month, etc.), orperhaps in response to certain events such as the accumulation of a newpiece of data. Such automatically generated reports may also be analyzedautomatically to identify patterns indicative of inefficiencies oranomalies in the process. Whenever such a pattern is identified pursuantto such automated analysis, one or more users may be notifiedautomatically, e.g., by e-mail, pager, fax, etc., so that they may knowto access one or more reports reflecting the identified pattern.Notified individuals may then, if appropriate, promptly take remedialmeasures and potentially avoid serious consequences resulting from theidentified anomaly. The reported information may further enable thenotified individuals to proactively avoid similar or related anomaloussituations in the future by manipulating or revamping various aspects ofthe process.

Data that may be used to perform the statistical analysis on theprocess, and report generation based thereupon, may be accumulated fromany of a number of sources, and may include one or more of (1) data fromsensors that monitor a physical or environmental condition of a productin a supply chain, (2) information concerning particular shipments andtheir contents (e.g., from ASN's), (3) and location event information(discussed below). Accordingly, huge amounts of raw data may beconsolidated into a simple, understandable form for easy identificationof problems or inefficiencies in the supply chain.

An example of a system 700 embodying several aspects of the presentinvention is depicted in FIG. 7. As shown, the system 700 may includeone or more sensors 102 which are associated with a quantity of product104 so as to monitor a physical or environmental condition of theproduct 104 as it passes through a supply chain. Sensors 102 may beassociated with the product 104 in any of a number of ways, and theinvention is not limited to any particular method of association. It isimportant only that each sensor 102 be arranged with respect to theproduct 104 so that, during transport, it is capable of monitoring thephysical or environmental characteristic of interest.

In some embodiments, the sensors 102 may be physically associated withthe product 104 so that they remain physically associated with theproduct 104 in substantially the same way (1) before and after theproduct 104 is loaded from a shipping location 106 or an intermediateholding area (not shown) onto a transport vehicle (e.g., a truck, ship,plane, train car, etc.—not shown), (2) before and after the product 104is transferred between transport vehicles, and/or (3) before and afterthe product 104 is unloaded from a transport vehicle to an intermediateholding area or the receiving location 108. Alternatively, one or morevehicles or holding areas may have one or more sensors 102 physicallyassociated with them so that the sensors 102 remain physicallyassociated with the vehicles or holding areas before and after theproduct 104 is loaded thereon or removed therefrom.

In alternative embodiments, other aspects or parameters of the shipmentprocess may additionally or alternatively be monitored. For example, thetrip time of each shipment, the trip distance for each shipment, thetime spent on each transporting vehicle, and/or the time spent at eachlocation before, after, or between shipments, may be measured and datareflecting the same may be accumulated for later analysis and/or reportgeneration. In such embodiments, sensors 102 would not be required, assuch variables could be measured simply by somehow determining when aproduct 104 left each shipping location 106 and arrived at eachreceiving location 108, and/or when a product was loaded onto andunloaded from each transporting vehicle. One way this may beaccomplished without using sensors 102 is, for example, through theinterrogation of RFID tags (not shown) or scanning of bar codes (notshown) associated with the product 104 at various locations. Suchinformation may alternatively be ascertained by using a tracking numberto access electronic records reflecting departure and arrival times,etc., that are maintained by the carrier responsible for a shipment.

For the sake of simplicity, the description that follows will focusprimarily on the use of one or more temperature sensors to tracktemperature conditions in a cold chain. It should be appreciated,however, that the invention is not so limited, and any of a number ofdifferent types of sensors 102 can be additionally or alternatively beemployed to track additional or different physical or environmentalconditions of the product 104 during any portion(s) or leg(s) of itssupply chain. The sensors 102 shown in FIG. 7 may, for example,additionally or alternatively be capable of monitoring humidity,incident light, exposure to x-rays or other radiation, pressure, shock,impact, presence or quantity of airborne particles, viscosity, volume,speed, acceleration, orientation, etc. In addition, as mentioned above,some embodiments of the invention need not employ sensors 102 at all,and instead may track and perform statistical analysis on an aspect ofthe shipping process unrelated to a physical or environmental conditionof the product, such as “trip time” for one of more legs of a shipment,time spent at particular product locations (e.g., at particularwarehouses or on particular vehicles) or the total distance traveledduring a shipment (i.e., “trip distance”).

As used herein, “cold chain” means any transportation chain for productin which the temperature of the environment in which the product is heldis controlled, and is not limited to situations in which products arekept “cold.”

In some embodiments, the sensors 102 may additionally or alternativelyidentify “alarm” conditions and log data reflecting times at which suchconditions occurred or were detected. Alarms may be generated, forexample, when a particular maximum or minimum temperature is exceeded,when a particular temperature condition is found to exist for more thana threshold period of time, etc.

The sensors 102 may additionally have some processing capability or“intelligence,” enabling them to calculate and log statistics such asaverage temperature, mean kinetic temperature, times above or belowthresholds, times within or without temperature ranges, etc. In someembodiments, specialized sensors capable of operating at cryogenictemperatures may be employed. An example of such a sensor is describedin U.S. Provisional Application Ser. No. 60/519,458, filed Nov. 11,2003, the entire disclosure of which is hereby incorporated herein byreference.

It should be appreciated that the system 700 may also include additionalequipment for monitoring the supply chain during multiple trips betweenthe shipping location 106 and the receiving location 108 and/or formonitoring the supply chain between additional shipping and/or receivinglocations, with relevant information concerning such other trips orother shipping routes also being communicated to the remote location 114in a similar fashion. As discussed in more detail below, theaccumulation of information from multiple trips and/or shipping routesin this manner enables the generation of statistical data, as well asuseful charts and graphs using the same, that can be monitored andanalyzed by trained personnel to identify problems or inefficiencies ina supply chain, and to evaluate how the performance of a supply chainmeasures up to certain industry benchmarks.

In contrast to the prior art system 100, the conditioning of the datalogged by the sensors 102 may be automated, i.e., may be performedwithout human intervention, so that the processing and compilation ofthat data into a useful format can also be performed automatically. Inthe embodiment of FIG. 7, this automatic conditioning may beaccomplished using any of a number of techniques, or some combinationthereof.

As a first option, the system 700 may employ RF units 702, 704 at theshipping and receiving locations. The RF units 702, 704 may, forexample, be RF receivers or transceivers configured and arranged toreceive RF signals from the sensors 102 at the beginning and end of thesupply chain being monitored. The received RF signals may containinformation that uniquely identifies the sensors 102 so as todistinguish them from other sensors 102. The temperature sensors 102 insuch an example may continuously or periodically transmit signals forreceipt by the RF units 702, 704, or may contain RFID transponders thatbroadcast RF signals in response to an interrogation signal from the RFunits 702, 704. In some embodiments, RFID transponders containing adynamic electronic product code (EPC), i.e., an EPC that can be altereddynamically to reflect a condition of the product 104, may be employedfor this purpose. An example of such a transponder suitable for such anapplication is described in U.S. Provisional Application Ser. No.60/475,554, the entire disclosure of which is hereby incorporated hereinby reference.

In the above situation, the RF units 702 and 704 may thereforecommunicate the starting and ending times of the supply chain for theproduct 104 to the database 112 at the remote location 114. In addition,the temperature data accumulated by the sensor 102 and ultimatelyuploaded to the database 112 may be time stamped. The database 112 maythus be caused to contain sufficient information, i.e., time stampeddata and the starting and stopping times for that data, to automaticallycondition the temperature data received from the sensor 102.

In some embodiments, location event information may additionally oralternatively be recorded in an RFID tag associated with the product104, either as a part of the sensor 102 or as a separate device.Examples of systems and techniques for accomplishing such a result aredescribed in U.S. Provisional Application Ser. No. 60/564,447, filed onApr. 22, 2004, the entire disclosure of which is hereby incorporatedherein by reference.

Alternatively, the RF units 702, 704 may simply transmit signals to thesensors 102 that instruct the sensors 102 to start and stop recordingtemperature data at the beginning and ending points of their journey. Inthis manner, the sensors 102 may themselves automatically condition thedata by recording data only during the relevant portion of the supplychain. As mentioned above, in some embodiments, the sensors 102 may evencontain sufficient “intelligence” to recognize and record the occurrenceof alarm conditions and/or to calculate and log statistical informationduring the journey. For example, the sensors 102 may record theoccurrence of alarm conditions in response to determining that minimumand/or maximum temperatures have been reached or have been exceeded fora particular time period, etc., and/or may calculate and log informationsuch as the average temperature, the mean kinetic temperature, timeabove or below a particular temperature or within or without aparticular temperature range.

The temperature data and/or location event information recorded by thesensors 102 may be communicated to the database 112 in any of a numberof ways, 20 and the invention is not limited to any particular mode ofcommunication. In some embodiments, the temperature data and/or locationevent information from the sensors 102 may be communicated from thesensors 102 to the RF unit 704 and/or intermediate RF units via RFsignals. Alternatively, the data and/or location event information maybe downloaded from the sensors 102 at the receiving location 108 (e.g.,using the downloading device 116), at the remote location 114, or atsome other location remote from the receiving location 108.

Additional RF units (not shown) may also be disposed at various pointsalong the supply chain to make available further information concerningtimes at which particular milestones, e.g., the leaving of a shippinglocation, the reaching a distribution center, the arrival at a receivinglocation or, the changing of hands from one carrier to another, or thetransfer from one vehicle to another are reached during the product'sjourney. In some embodiments, data and/or recorded location eventinformation may also be downloaded from the sensors 102 at theseintermediate locations. As discussed in more detail below, informationconcerning the times and locations at which such milestones occur,hereafter “location event information,” may be advantageously usedtogether with other data stored in the database 112 for the purpose ofanalyzing the performance of the supply chain and various legs thereof.For instance, location event information may be used together withtime-stamped temperature data to determine the average temperature aproduct was subjected to while it was disposed in a particular warehouseor on a particular vehicle. A similar determination may additionally oralternatively be made based upon the “marking” of temperature data (ordata accumulated by some other type of sensor) with location eventinformation, as described in U.S. Provisional Patent Application Ser.No. 60/564,447, incorporated by reference above.

For some applications, an RF unit 702, 704 at only one of the twolocations 106, 108 could be used if it were known that either thebeginning or ending part of the data from the monitors 108 would notneed conditioning, for example, if it was known that the pressing of astart or stop button on the sensors 102 would be performed in a reliablemanner at one of the two locations.

A second approach that may be employed, either alone or in combinationwith one or more aspects of the RF approach discussed above, toautomatically condition data received from the sensors 102 is to obtainlocation event information from the carrier(s) that shipped the product104. With reference to FIG. 7, one way this may be accomplished is tomake one or more tracking numbers, e.g., a FedEx tracking number,available to the server 126 at the remote location 114, which enablesthe server 126 to retrieve location event information from one or moreservers 124 maintained by the carrier(s) responsible for transportingthe product 104. If the data logged by the sensors 102 is time stamped,the location event information retrieved from the server 124 can itselfbe used to automatically condition the data, because the location eventinformation would provide, among other information, the time at whichthe product 104 left the shipping location 106 and the time at which itarrived at the receiving location 108.

Moreover, as noted above, in addition to or in lieu of such conditioningof data, location event information may be used to select particularportions of conditioned data that should be used to generate particularreports. For example, if a sensor 102 was associated with a product 104from when it left a shipping location 106 to when it arrived at areceiving location 108, after having passed through several distributioncenters and/or been transferred between many vehicles, location eventinformation could be used to identify a portion of the sensor's datacorresponding to a time period that the product 104 was at a particulardistribution center or was on a particular vehicle.

As further elaborated on below, this and other location eventinformation obtained from the carrier may additionally or alternativelybe included on one or more of the charts and/or graphs generated for auser so as to provide the user with insight into how particular locationevents impacted the process being monitored. It should be appreciatedthat such location event information can additionally or alternativelybe obtained in other ways, and the invention is not limited to the useof any particular technique for obtaining it. For example, as mentionedabove, the RF units 702, 704 can be used to obtain location eventinformation, or to cause RFID tags associated with products to storelocation event information transmitted by such units.

Additionally or alternatively, RFID tags or barcodes may be associatedwith products so that, as the products pass through certain locations atwhich RFID interrogators or bar code readers are disposed or employed,location event information for the products may be accumulated bytracking locations and times at which the RFID tags or bar codes areread. As another option, the sensors 102 may additionally oralternatively be equipped or associated with GPS receivers that trackthe location of a product versus time. In some embodiments, people couldadditionally or alternatively manually provide information concerninglocation events by keying appropriate data into a computer system asproducts pass through particular locations, or a paper based systemcould even be employed, with people creating documents reflectinglocation events for products as the products pass through particularlocations, and information from such documents later being manuallyentered into a computer system.

In some embodiments, accumulated location event information, and perhapsother information as well, may additionally or alternatively be employedto authenticate the pedigree and integrity of transported items. Systemsand techniques for accomplishing such a result are described in U.S.Provisional Application Ser. No. 60/564,402, filed Apr. 22, 2004, theentire disclosure of which is hereby incorporated herein by reference.

Moreover, in some embodiments, techniques may additionally oralternatively be employed to predict the remaining shelf life of aproduct based upon accumulated sensor data. Examples of systems andmethods for doing this are described in U.S. Provisional ApplicationSer. No. 60/526,878, filed Dec. 4, 2003, the entire disclosure of whichis hereby incorporated herein by reference.

As mentioned above, it is common for a shipper to transmit an advancedshipping notification (ASN) to a receiver in advance of shipping aquantity of product via one or more carriers. In one embodiment of thepresent invention, this ASN may also be transmitted, e.g., via a networkcloud 706 (FIG. 7), from the computer 118 at the shipping location 106to the database 112 at the remote location 114, or may be forwarded fromthe computer 120 at the receiving location 108 to the database 112 afterbeing received from the shipping location 106.

It should be appreciated that the network cloud 706 depicted in theexample shown may be any network or combination of separate orintegrated networks, or other communication links, suitable fortransmitting information between and/or amongst the variouscomputers/servers 118, 120, 124, 126 in the system. The network cloud706 may, for example, represent the Internet. It should further beappreciated that each of the computers/servers 118, 120, 124, 126 mayitself comprise multiple computers operating together, or may be a partof a larger computer system or network. As shown in the example ASNformat attached as Appendix A to U.S. Provisional Application Ser. No.60/500,565, incorporated by reference above, the ASN may include a widevariety of details concerning the product 104 being shipped, the mannerin which the shipment is to take place, and the entities responsible foror involved in buying, selling, and shipping the product 104.

In accordance with one aspect of the present invention, the ASN may bemodified or appended to contain one or more codes that uniquely identifythe sensors 102 that are associated with the product 104 being shipped.The codes may, for example, be serial numbers unique to the sensors 102,or may be unique “trip numbers,” which would enable the same sensors 102to each be used for multiple trips. In some embodiments, the ASN mayalso include information reflecting where the sensors 102 are physicallypositioned with respect to the product 104 and/or with respect to thecontainer and/or vehicle in which the product 104 is transported. Forexample, the ASN may specify that a sensor 102 is disposed on the lowerleft-hand side of pallet number three, in container number thirty seven,within truck number nine hundred and eighty two. In addition, as notedabove, the ASN may contain tracking code(s) that may be used to retrievelocation event information from the carrier(s) responsible for shippingthe product 104.

In alternative embodiments, the ASN stored in the database 112 may belinked to data from a sensor 102 in ways other than by including sensoridentification information in the ASN. For example, information thatlinks the data from one or more sensors 102, or other data reflecting amonitored physical or environmental condition or location of a product104, to a particular ASN may instead be transmitted and stored in thedatabase 112 along with that data. The document identifier field “206”of the example ASN format shown in Appendix A of U.S. ProvisionalApplication Ser. No. 60/500,565 (incorporated by reference above) may,for example, be stored along with sensor data to provide such a link.

As discussed in more detail below, because ASN information may be storedin the database 112 and linked with condition (e.g., temperature) and/orother information (e.g., trip time, time spent at particular productlocations, and/or trip distance) for a particular shipment of product104, any of the numerous details reflected in the ASN may be used togenerate reports for review by a person monitoring one or morecharacteristics of the shipment. In addition, the ASN information may bemade available to the reviewer as he or she is reviewing those reports,so as to provide easy access to information respecting a particularshipment, upon request, in a format with which the reviewer is alreadyfamiliar.

After conditioned data has been stored in the database 112 along with atleast some information indicating certain parameters of the trips withwhich the conditioned data is associated, e.g., using ASN informationand/or location event information, a number of charts, graphs, and otherreports may be generated based upon that data to provide personnelreviewing them with useful insight into various aspects of the processbeing monitored (e.g., the cold chain). In one embodiment, data from alllegs of a company's supply and distribution network can be accumulatedas matter of course, so that reports may be generated for a user,automatically or upon request, with one or more parameters for suchreports being selected by the user. For example, a person may use acomputer 128 (FIG. 7) to access a website maintained on the server 126at the remote location 114, and through that website may select from alist of standard report options, or may request that a customized reportbe prepared.

In response to the user's selections, the server 126 may identify andretrieve the data from the database 112 that should be used ingenerating the report, and then generate the requested report based onthe selected data. This should be contrasted with the prior art system100 discussed above, in which reports were generated in advance,independent of the user accessing the website on the server 126, therebygiving the user access to only a predefined set of pre-generated chartsand graphs via corresponding hyperlinks.

An example of a menu that may be presented to a user for the purpose ofspecifying various attributes of a report to be generated is shown inFIG. 8. In the example shown, the user is permitted to select (usingfields 802 a-b, and 804 a-b) date ranges for the data that will be usedin generating the report. In addition, the user is permitted to select(using fields 806 a-d) from among a number of other criteria upon whichdata from the database 112 may be selected for inclusion. As shown, eachof the fields 806 a-d may be provided with a pull-down menu that permitsthe user to select from among a group of predefined criteria. Thepredefined criteria that are made available may be determined for aparticular user based upon the data that has been stored in the database112, and the particular needs of that user.

In embodiments in which ASN information for a shipment of product 104 isstored in the database 112 and linked with information reflectingchanges in a physical or environment al condition or location of theproduct 104 during the shipment, the predetermined criteria used may beany of the large number of attributes for the shipment reflected in theASN. In the illustrative example shown, the criteria available forselection in the fields 806 a-d include “product type,” “supplier,”“distribution center,” “carrier,” and “product location” (e.g., aparticular warehouse or vehicle). In alternative embodiments, a largeror smaller number of criteria, or even all possible criteria, by whichthe data in the database 112 can be segregated, conceivably by using anyfield contained in the ASN associated with that data, can be presentedto the user as possible data selection criteria.

As shown, when the user selects any one of the criteria, the possiblevalues associated with that criterion may be presented in acorresponding field 808 a-d. In some embodiments, the possible valuespresented in the fields 808 a-d may be automatically extracted from thestored ASN information, based on the selection made in the correspondingfield 806 a-d. In other embodiments, the content of the fields 808 a-dmay be determined ahead of time.

In any event, in the example shown, the selection of “product” in thefield 806 a has caused a number of different types of products to bedisplayed in the field 808 a, and the user has selected “fresh cutsalads.” In addition, the user has indicated that only data collectedbetween the dates of Dec. 1, 2002 and Mar. 11, 2003 should be used forgeneration of both control charts and box plots/histograms. Although notillustrated, the user could alternatively have selected one or moreadditional or different criteria in the fields 806 a-d for use inselecting the data to be used in generating one or more reports. Forexample, the user may have specified that only data corresponding withshipments of “mixed vegetables” that were shipped by “PDQ Carrier” from“ABC Supplier” to “XYX Distribution Center” should be used in generatingreports. In the example shown, after the user has made the desiredselections in fields 802-808, the user may click on a button 810 torequest that reports be generated using data selected in accordance withthe specified criteria.

FIG. 9 shows an illustrative example of how a chart or graph may befirst be presented to the user in response to the user clicking on thebutton 810 (FIG. 8). As shown, information may be displayed in a firstregion 914 of the screen that reflects the “settings” that have beenselected by the user (FIG. 8). In addition, in a second region 916 ofthe screen, the user may be given a number of options that permit theuser to select or alter the formatting and configuration of generatedcharts and/or graphs.

As a first option in the illustrated example, the user may select (usingpull-down menu 902) a variable that the generated chart or graph will bebased upon. In the example shown, the user has selected “meantemperature” as the analysis variable. It should be appreciated,however, that any of a virtually unlimited number of analysis variablescan additionally or alternatively be used, and the invention is notlimited to the use of any particular variable. As but a few examples, inaddition to or lieu of “mean temperature,” the user could have selectedminimum temperature, maximum temperature, degree-minutes above athreshold temperature, degree-minutes below a threshold temperature,time above a threshold temperature, time below a threshold temperature,trip time, trip distance, or time spent at product locations (e.g., aparticular warehouse or vehicle). Moreover, in embodiments in whichconditions other than temperature are additionally or alternativelymonitored, e.g., humidity, incident light, exposure to x-rays or otherradiation, pressure, shock, impact, presence or quantity of airborneparticles, viscosity, volume, speed, acceleration, orientation, etc.,similar variables related to such conditions may be presented as optionsin field 902.

Next, in the example of FIG. 9, the user is given the option ofselecting from among a number of types of charts and graphs that may begenerated and displayed. Any of a number of different types of charts orgraphs may be used, and the invention is not limited to any particulartype of chart or graph. In the illustrative example shown, the user ispermitted to select a “control chart,” a “histogram,” or a “box plot”for display. The basic format for these three types of charts and graphsmay be essentially the same as that discussed above in the “Background”section of this disclosure.

For the box plot option, the user may be permitted to select the basisupon which the generated box plots will be segregated and/or sorted. Inthe example shown in FIG. 9, this functionality is enabled by thepull-down menus 906 a-b. Specifically, the pull-down menu 906 a maypermit the user to select a criterion that determines how the generatedbox plots will be segregated, and the pull-down menu 906 b may determinehow the segregated box plots will be sorted on the screen. In theexample shown, the user has requested that the box plots be segregatedby product carrier, i.e., that a separate box plot be generated for eachcarrier that transported “fresh cut salads” between Dec. 1, 2002 andMar. 11, 2003, and has requested that the generated box plots be sortedbased upon the ranges of data values included in them.

Like the criteria available for selection in the fields 806 a-d, the boxplots may be segregated in any number of ways, and the invention is notlimited for any particular criterion for segregation. They may, forinstance, be segregated by “product,” “supplier,” “distribution center,”“carrier,” “product location,” or any other distinguishing criterion.

As a result of these selections, the generated chart 908 shows arespective box plot for each of 17 different carriers (A-Q) that shippedthe selected product during the selected dates, with the box plots beinggenerated based upon the mean temperature values measured during thoseshipments. In this example, the box plots are sorted so that the boxplot for the carrier having the smallest range between the highest andlowest mean temperature values (i.e., carrier Q) is on the left and thecarrier having the largest range between the highest and lowest meantemperature values (i.e., carrier A) is on the right.

As an option, the user may click on the “select categories” button 910,so as to be given the ability to select a smaller group, or even one, ofthe displayed box plots for display. This may be useful, for example, ifthe number of box plots displayed causes the box plots to be so smallthat they are difficult to evaluate, or if only a few different boxplots are of interest. In addition, the high and low values of theselected variable (mean temperature in the example shown) may be alteredusing the fields 912 a-b.

When box plots such as those shown it FIG. 9 are displayed, the user mayclick on one of them to drill down to one or more charts or graphsgenerated based on the same data upon which the clicked-on box plot wasbased. FIG. 10 illustrates an example of one such graph (a controlchart) that may be displayed when a box plot (carrier “F” in the exampleshow) is clicked on in this manner. As shown, in addition to displayinga control chart for the selected variable (mean temperature), thesettings information in region 914 may indicate that data from carrier“F” has been selected. At this stage, the user may also click on thehistogram button 904 b or the box plot button 904 c and have displayed ahistogram or box plot that is also limited to the data from carrier “F.”It should be appreciated that these same box plots, control charts, andhistograms could also have been generated if “carrier” had been selectedin one of the fields 806 b-d, and “F” had been selected in thecorresponding one of the fields 808 b-d when the menu of FIG. 8 waspresented to the user. When viewing the box plot under the abovecircumstances (i.e., after having clicked on a box plot for a particularcarrier), the user may click on the select categories button 910 so asto re-broaden the box plot chart to include box plots for additional, orperhaps all, carriers once again.

Referring again to FIG. 10, the analysis variable (field 902) may bealtered (e.g., to “time above a threshold,” or “trip time,” etc.), so asto alter the informational content of the control chart displayed. Thedata to be used in generating such a modified graph would be selectedbased upon the content of the settings information in the region 914. Inaddition, the high and low values of the selected variable and thenumber of data points displayed on the screen may be altered using thefields 912 a-b and 1004, respectively, and the portion of the graph thatis displayed may be altered using the buttons 1004 a-d.

In some embodiments, by clicking on one of the data points in a controlchart such as that shown in FIG. 10, the user may drill down to a graphreflecting data upon which the clicked-on data point was based. Thegraph of FIG. 11 shows an example of how temperature profile datacorresponding to a point on the graph of FIG. 10 may appear. In theexample shown, the graph represents the temperature that was monitoredduring a particular shipment of “fresh cut salads” transported by thecarrier “F.”

In addition to or in lieu of such a graph, much useful information (notshown) concerning details of the monitored shipment may be displayed.Indeed, virtually any information from a corresponding ASN stored in thedatabase 112, and/or stored location event information, may be displayedfor review at this stage. Moreover, in addition to or in lieu of theforegoing, summary data extracted from the downloaded sensor data forthe selected trip or product location (e.g., a particular warehouse orvehicle) may be displayed to the user when one of the points on acontrol chart is clicked on or otherwise selected.

FIG. 12 illustrates an example of another type of chart (a histogram)that may be displayed when one of a number of displayed box plots(carrier “F” in the example shown) is clicked on. For a histogram, thevalue in the “number of bins” field 1202 may be used to determine thenumber of “bins” (or bars) that the histogram will include. In addition,like in the previous examples, the analysis variable in field 902, aswell as the values in the fields 912 a-b, may be altered to alter thecontent and appearance of the histogram.

Referring back to FIG. 9, when the “histogram” button 904 b or “controlchart” button 904 a is clicked on directly, without first drilling downfrom the displayed box plots, a histogram like that shown in FIG. 12 maybe generated based on all of the data selected from the menu of FIG. 8,rather than from only a selected portion of that data. The histogramgenerated in such a circumstance may therefore include, for example,data from all carriers that transported “fresh cut salad” during theselected dates, rather than data from only a single carrier. In such acase, the information displayed in the current settings region 914 maybe caused to indicate that data from “all” carriers has been selected.

As discussed above, in addition to ASN information, location eventinformation, either retrieved from a carrier or gathered in some othermanner, may also be stored in the database 112 and used during thegeneration of reports. An example of how such a report may appear isillustrated in FIG. 13. The graph and chart of FIG. 13 togethercommunicate useful information not only concerning the temperatureprofile measured during shipment of a product between a shippinglocation (origin) and a receiving location (destination), but alsoconcerning the times and places at which certain events occurred (e.g.,times at which the monitored product was loaded onto and off ofparticular airplanes or other vehicles). In some embodiments, detailedinformation concerning the shipment with which the graph is associated,e.g., the information under the heading “Shipment Information” in FIG.13, may be derived from the ASN information stored in the database 112.

As noted above, in addition or in lieu of generating reports in responseto user requests, at least some reports may be generated automaticallyon a regular basis (e.g., once a day, once a week, once a month, etc.),or perhaps in response to the occurrence of certain events, such as theaccumulation of a new piece of data, or ten new pieces of data, etc. Inaddition, for at least some types of reports, e.g., a control chart suchas that shown in FIG. 10, the automatically generated reports may alsobe automatically analyzed after they are generated so as toautomatically identify patterns indicative of inefficiencies oranomalies in the process. Examples of patterns in a control chart thatmay be indicative of potentially anomalous conditions or trends arediscussed above in the “Background” section of this disclosure. Itshould be appreciated, however, that in contrast to the prior art systemdiscussed above, embodiments of the present invention that perform suchautomated analysis of control charts or other reports do not require ahuman being to visually inspect the reports to identify such patterns,thereby greatly increasing the efficiency and effectiveness of theanalysis process. It should further be appreciated that any of a numberof other patterns or conditions could additionally or alternatively belooked for, and the invention is not limited to the particular patternsmentioned herein.

In some embodiments, whenever such a pattern or condition is identifiedpursuant to such automated analysis, one or more users may be notifiedautomatically, e.g., by e-mail, pager, fax, phone, etc., so that theymay promptly and efficiently access one or more reports reflecting theidentified pattern. In the case of e-mail, the user may be provided withan appropriate hyperlink enabling the user to quickly access the reportcontaining the identified pattern. Notified individuals may then, ifappropriate, promptly take remedial measures and potentially avoidserious consequences resulting from the identified anomaly. The reportedinformation may further enable the notified individuals to proactivelyavoid similar or related anomalous situations in the future bymanipulating or revamping various aspects of the process.

In some embodiments, each notified individual may be given theopportunity to respond to the notification with an acknowledgementindicating that they received it. The acknowledgment may optionallyinclude information such as the time it was transmitted, the nature orseverity of the condition—possibly assessed after the individual hasreviewed one or more reports, and what corrective action should be orhas been taken. When an acknowledgement is not received within a certainperiod of time, one or more other individuals, e.g., a supervisor, maybe automatically notified in an effort to escalate the issue and ensurethat the condition is dealt with promptly and appropriately.

As another option, the above-discussed reports may be automaticallygenerated so as to take into account data accumulated during some pastperiod. For example, the control limits of a control chart may beadjusted based on data accumulated during the previous week, thirtydays, sixty days, etc., so as to more accurately differentiate betweencommon cause variations and “out of control” points. Moreover, alarmnotifications may be sent to appropriate individuals (as discussedabove) if the control limits are automatically adjusted too far, e.g.,if one of them was assigned a value outside of a predefined acceptablerange.

Having thus described at least one illustrative embodiment of theinvention, various alterations, modifications and improvements willreadily occur to those skilled in the art. Such alterations,modifications and improvements are intended to be within the spirit andscope of the invention. Accordingly, the foregoing description is by wayof example only and is not intended as limiting. The invention islimited only as defined in the following claims and the equivalentsthereto.

1. A method, comprising steps of: (a) storing, in memory accessible to at least one processor, an electronic copy of an advanced shipping notification (ASN) that was transmitted from a shipping location to a receiving location in advance of shipping at least one item therebetween; (b) storing, in memory accessible to the at least one processor, data reflecting at least one determined value of at least one monitored aspect of the shipment between the shipping location and the receiving location; and (c) with the at least one processor, using at least some information from the ASN stored in memory, together with at least a portion of the data stored in memory, to generate at least one report involving the at least one determined value.
 2. The method of claim 1, wherein the at least one monitored aspect of the shipment comprises at least one physical or environmental condition of the at least one item during the shipment.
 3. The method of claim 2, wherein the ASN comprises an identifier that identifies a sensor used to monitor the at least one physical or environmental condition of the at least one item during the shipment.
 4. The method of claim 1, wherein the step (c) further comprises: using a tracking number included in the ASN to retrieve location event information from the carrier responsible for transporting the at least one item between the shipping location and the receiving location; and combining the retrieved location event information with the data to generate the at least one report.
 5. The method of claim 4, wherein the at least one report comprises at least one of a chart and a graph on which information concerning the at least one determined value and information concerning at least one location event identified from the retrieved location event information are displayed together.
 6. The method of claim 2, wherein the step (c) further comprises: using a tracking number included in the ASN to retrieve location event information from a carrier responsible for transporting the at least one item between the shipping location and the receiving location; and using the retrieved location event information to identify at least a portion of the data as having been accumulated by at least one sensor when the at least one sensor was associated with the at least one item.
 7. The method of claim 6, further comprising a step of: (d) extracting summary information from the identified portion of the data.
 8. The method of claim 7, wherein the summary information comprises at least one of a mean temperature, a minimum temperature, a maximum temperature, degree-minutes above a threshold temperature, degree-minutes below a threshold temperature, time above a threshold temperature, and time below a threshold temperature.
 9. The method of claim 7, wherein the summary information reflects the at least one physical or environmental condition of the at least one item at only a single product location.
 10. The method of claim 2, wherein the at least one monitored aspect of the shipment comprises a temperature of an environment in which the at least one item was disposed.
 11. A database having stored therein an electronic copy of an advanced shipping notification (ASN) that was transmitted from a shipping location to a receiving location in advance of shipping at least one item therebetween, and further having stored therein data reflecting at least one determined value of at least one monitored aspect of the shipment of the at least one item.
 12. The database of claim 11, wherein the at least one monitored aspect of the shipment comprises at least one physical or environmental condition of the at least one item during the shipment.
 13. The database of claim 11, wherein the ASN comprises an identifier that identifies a sensor used to monitor the at least one monitored condition of the shipment.
 14. The database of claim 11, wherein the at least one monitored aspect of the shipment comprises a temperature of an environment in which the at least one item was disposed.
 15. A computer-readable medium for use with at least one processor included in a system including at least one memory accessible to the at least one processor that has stored therein an electronic copy of an advanced shipping notification (ASN) that was transmitted from a shipping location to a receiving location in advance of shipping at least one item therebetween, and having further stored therein data reflecting at least one determined value of at least one monitored aspect of the shipment of the at least one item, the computer-readable medium having a plurality of instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to perform a step of: (a) using at least some information from the ASN stored in memory, together with at least a portion of the data stored in memory, to generate at least one report involving the at least one determined value.
 16. The computer-readable medium of claim 15, wherein the at least one monitored aspect of the shipment comprises at least one physical or environmental condition of the at least one item during the shipment.
 17. The computer-readable medium of claim 16, wherein the ASN comprises an identifier that identifies a sensor used to monitor the at least one physical or environmental condition of the at least one item during the shipment.
 18. The computer-readable medium of claim 15, wherein the step (a) further comprises: using a tracking number included in the ASN to retrieve location event information from a carrier responsible for transporting the at least one item between the shipping location and the receiving location; and combining the retrieved location event information with the data to generate the at least one report.
 19. The computer-readable medium of claim 18, wherein the at least one report comprises at least one of a chart and a graph on which information concerning the at least one determined value and information concerning at least one location event identified from the retrieved location event information are displayed together.
 20. The computer-readable medium of claim 16, wherein the step (a) further comprises: using a tracking number included in the ASN to retrieve location event information from a carrier responsible for transporting the at least one item between the shipping location and the receiving location; and using the retrieved location event information to identify at least a portion of the data as having been accumulated by at least one sensor when the at least one sensor was associated with the at least one item.
 21. The computer-readable medium of claim 20, having additional instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to perform the further step of: (b) extracting summary information from the identified portion of the data.
 22. The computer-readable medium of claim 21, wherein the summary information comprises at least one of a mean temperature, a minimum temperature, a maximum temperature, degree-minutes above a threshold temperature, degree-minutes below a threshold temperature, time above a threshold temperature, and time below a threshold temperature.
 23. The computer-readable medium of claim 21, wherein the summary information reflects the at least one physical or environmental condition of the at least one item at only a single product location.
 24. The computer-readable medium of claim 16, wherein the at least one monitored aspect of the shipment comprises a temperature of an environment in which the at least one item was disposed.
 25. A method, comprising: transmitting an advanced shipping notification (ASN) from a shipping location to a receiving location in advance of shipping at least one item therebetween, the ASN comprising an identifier that identifies a sensor that will be used to monitor at least one physical or environmental condition of the at least one item as the item is transported between the shipping location and the receiving location.
 26. The method of claim 25, wherein the ASN further comprises information identifying a location of the at least one sensor with respect to at least one of the at least one item and a vehicle on which the at least one item is to be transported.
 27. An advanced shipping notification (ASN) that is transmitted from a shipping location to a receiving location in advance of shipping at least one item therebetween, the ASN comprising an identifier that identifies a sensor that will be used to monitor at least one physical or environmental condition of the at least one item as the item is transported between the shipping location and the receiving location.
 28. The ASN of claim 27, further comprising information identifying a location of the at least one sensor with respect to at least one of the at least one item and a vehicle on which the at least one item is to be transported. 